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This function "cleans" names of model parameters by removing patterns like "r_" or "b[]" (mostly applicable to Stan models) and adding columns with information to which group or component parameters belong (i.e. fixed or random, count or zero-inflated...)

The main purpose of this function is to easily filter and select model parameters, in particular of - but not limited to - posterior samples from Stan models, depending on certain characteristics. This might be useful when only selective results should be reported or results from all parameters should be filtered to return only certain results (see print_parameters()).


clean_parameters(x, ...)



A fitted model.


Currently not used.


A data frame with "cleaned" parameter names and information on effects, component and group where parameters belong to. To be consistent across different models, the returned data frame always has at least four columns Parameter, Effects, Component and Cleaned_Parameter. See 'Details'.


The Effects column indicate if a parameter is a fixed or random effect. The Component can either be conditional or zero_inflated. For models with random effects, the Group column indicates the grouping factor of the random effects. For multivariate response models from brms or rstanarm, an additional Response column is included, to indicate which parameters belong to which response formula. Furthermore, Cleaned_Parameter column is returned that contains "human readable" parameter names (which are mostly identical to Parameter, except for for models from brms or rstanarm, or for specific terms like smooth- or spline-terms).


# \donttest{
model <- download_model("brms_zi_2")
#>                     Parameter Effects     Component              Group
#> 1                 b_Intercept   fixed   conditional                   
#> 2                   b_persons   fixed   conditional                   
#> 3                     b_child   fixed   conditional                   
#> 4                    b_camper   fixed   conditional                   
#> 5      r_persons[1,Intercept]  random   conditional Intercept: persons
#> 6      r_persons[2,Intercept]  random   conditional Intercept: persons
#> 7      r_persons[3,Intercept]  random   conditional Intercept: persons
#> 8      r_persons[4,Intercept]  random   conditional Intercept: persons
#> 9       sd_persons__Intercept  random   conditional    SD/Cor: persons
#> 10             b_zi_Intercept   fixed zero_inflated                   
#> 11                 b_zi_child   fixed zero_inflated                   
#> 12                b_zi_camper   fixed zero_inflated                   
#> 13 r_persons__zi[1,Intercept]  random zero_inflated Intercept: persons
#> 14 r_persons__zi[2,Intercept]  random zero_inflated Intercept: persons
#> 15 r_persons__zi[3,Intercept]  random zero_inflated Intercept: persons
#> 16 r_persons__zi[4,Intercept]  random zero_inflated Intercept: persons
#> 17   sd_persons__zi_Intercept  random zero_inflated    SD/Cor: persons
#>    Cleaned_Parameter
#> 1        (Intercept)
#> 2            persons
#> 3              child
#> 4             camper
#> 5          persons.1
#> 6          persons.2
#> 7          persons.3
#> 8          persons.4
#> 9        (Intercept)
#> 10       (Intercept)
#> 11             child
#> 12            camper
#> 13         persons.1
#> 14         persons.2
#> 15         persons.3
#> 16         persons.4
#> 17       (Intercept)
# }